This section provides background information related to the present disclosure which is not necessarily prior art.
In the Internet environment, after a user logs on to a web social platform such as Weibo, if a server of the web social platform can recommend applications that the user is interested in, it not only enhances user experience, but also improves the access and click-through rate of the application.
The existing technology mainly uses two application recommending methods. The first method recommends applications on the basis of an interest category of a user and category attributes of applications, that is, an interest category of a user is determined according to behavior data (such as a historical access record) of the user, and then, a degree of similarity between the interest category of the user and each optional application to be recommended is calculated, so as to determine a to-be-recommended application according to the degree of similarity. The second method recommends applications on the basis of users having similar interests, that is, an interest of a user is analyzed first, then, according to a k-nearest neighbor algorithm or Support Vector Machine (SVM), users having interests similar to that of the user are searched for in all registered users on the website, and applications visited or downloaded by these users having similar interests are considered comprehensively, to determine a to-be-recommended application.
The existing technology at least has the following challenge. Both the application recommending methods described above only recommend applications roughly according to interests of users, and therefore, have a defect of low recommendation accuracy.